Obtaining relevant information from multiple large lists of records can be relatively straightforward in some situations. One particular situation is when records in separate lists are similar and it is desired to obtain information in the records having a particular value or character string in a particular field. The fields at issue can be isolated using filtering functions of data interfacing software and the desired information retrieved. By using combinations of filtering functions, more sophistication can be provided to the way in which fields are identified for comparison. Once compared, some records can be isolated based on the comparisons on the particular fields. The isolated records can then be aggregated so as to provide a report including all the records that together constitute the desired information.
But in order to recognize common records, such filtering functions rely on identical fields across the records. In the real world, lists may have no identical fields across the records, despite those records being related, or can have identical fields in a relatively small number of fields (or parts of fields) such that existing filtering functions are unable to provide isolation of the desired records from other records. For example, such problems can occur when a list has records originating from a number of different sources. This problem only worsens as the size of lists becomes larger (e.g., a list having billions of records), in terms of the number of records present. With the sizes of lists in the real world increasing as time progresses, this problem is expected to worsen over time.